Data models may be used to organize or structure data about one or more business systems. Over time, as businesses grow and become more sophisticated, so too may their data models grow as well. This growth of the data models may cause the data models to develop redundancies, deep levels of recursive hierarchies and other inefficiencies. Such inefficiencies also may be introduced when two or more data models are merged into one such as, for example, when one business buys or merges with another.
One of the challenges in removing these inefficiencies from the data models is that often times the names and structure of the data model may be closely integrated with the semantics and linguistics of the particular business system. It would be beneficial to have a system capable of identifying semantic similarities and/or differences across one or more data models based on the semantic meanings of the objects, to optimize (e.g., identify and remove these inefficiencies) one or more data models.